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Commit 5b9000ac authored by nmostafa's avatar nmostafa
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[MLIR] Fix issues after rebase on ngraph/master

parent 8cb95f71
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Showing with 690 additions and 103 deletions
src/contrib/mlir/compiler.cpp
View file @ 5b9000ac
......@@ -22,7 +22,7 @@
#include "lowerer.hpp"
#include "ngraph/descriptor/tensor.hpp"
#include "ngraph/graph_util.hpp"
#include "ngraph/node_vector.hpp"
#include "ngraph/node.hpp"
#include "ngraph/op/add.hpp"
#include "ngraph/op/dot.hpp"
#include "ngraph/op/experimental/compiled_kernel.hpp"
......
src/contrib/mlir/memory_manager.cpp
View file @ 5b9000ac
......@@ -15,12 +15,9 @@
//*****************************************************************************
#include "memory_manager.hpp"
#include "ngraph/ngraph_visibility.hpp"
#include <memory>
using namespace ngraph::runtime::ngmlir;
/// Call back to allocate memory for temps from JIT'ed code
......
src/ngraph/op/tan.cpp
View file @ 5b9000ac
//*****************************************************************************
// Copyright 2017-2019 Intel Corporation
//
......
src/ngraph/pass/mlir_subgraph_extraction.cpp
View file @ 5b9000ac
......@@ -20,6 +20,7 @@
#include "ngraph/op/dot.hpp"
#include "ngraph/op/experimental/compiled_kernel.hpp"
#include "ngraph/op/get_output_element.hpp"
#include "ngraph/assertion.hpp"
using namespace ngraph::descriptor;
using namespace ngraph::op;
......@@ -64,9 +65,9 @@ bool MLIRSubgraphExtractionPass::run_on_function(std::shared_ptr<Function> func)
auto& out_desc = output_descs[0];
// 'replace_output' invalidates iterator of the original container. Use a copy instead.
std::set<Input*> input_descs{out_desc.get_inputs()};
const std::set<descriptor::Input*> input_descs = out_desc.get_inputs();
for (Input* in_desc : input_descs)
for (descriptor::Input* in_desc : input_descs)
{
in_desc->replace_output(ck, i);
}
......
src/ngraph/runtime/cpu/builder/matmul_bias.cpp
View file @ 5b9000ac
......@@ -38,14 +38,6 @@ namespace ngraph
auto arg1_buffer_index = external_function->get_buffer_index(args[1].get_name());
auto out0_buffer_index = external_function->get_buffer_index(out[0].get_name());
// TODO: Quick hook for MLIR.
if (std::getenv("NGRAPH_MLIR") != nullptr)
{
functors.emplace_back(build_mlir_single_output_binary_op(
node, arg0_tensor, arg1_tensor, out0_tensor));
return;
}
const ngraph::op::MatmulBias* mm = static_cast<const ngraph::op::MatmulBias*>(node);
const auto& arg0_shape = mm->get_a_shape();
......
src/ngraph/runtime/cpu/builder/mlir_cpu_compiled_kernel.cpp
View file @ 5b9000ac
......@@ -40,29 +40,31 @@ namespace ngraph
// Tensors haven't been allocated yet so we have to keep a pointer to the pointer
// that will hold the future memory address.
std::vector<void**> double_ptr_args;
std::vector<size_t> buffer_indices;
for (const TensorViewWrapper& arg : args)
{
double_ptr_args.push_back(&external_function->get_tensor_data(arg.get_name()));
auto buffer_index = external_function->get_buffer_index(arg.get_name());
buffer_indices.push_back(buffer_index);
}
for (const TensorViewWrapper& result : out)
{
double_ptr_args.push_back(
&external_function->get_tensor_data(result.get_name()));
auto buffer_index = external_function->get_buffer_index(result.get_name());
buffer_indices.push_back(buffer_index);
}
// Create functor that will be executed to compile and run this CompiledKernel.
// Note that 'double_ptr_args' must be captured by value since it's a local var.
auto functor = [node, double_ptr_args](CPURuntimeContext* ctx,
auto functor = [node, buffer_indices](CPURuntimeContext* ctx,
CPUExecutionContext* ectx) {
// MLIR requires a list of type-erased pointer to arguments. Tensors must have
// been allocated at this point so we can get rid of the extra reference.
std::vector<void*> ptr_args;
for (auto& double_ptr : double_ptr_args)
for (auto& buffer_index : buffer_indices)
{
ptr_args.push_back(*double_ptr);
ptr_args.push_back(ctx->buffer_data[buffer_index]);
}
// Compile nodes within the CompiledKernel op.
......
src/ngraph/runtime/cpu/cpu_builder.cpp
View file @ 5b9000ac
......@@ -103,7 +103,6 @@
#include "ngraph/runtime/cpu/kernel/subtract.hpp"
#include "ngraph/runtime/cpu/kernel/tan.hpp"
#include "ngraph/runtime/cpu/kernel/tanh.hpp"
#include "ngraph/runtime/cpu/mlir/compiler.hpp"
#include "ngraph/runtime/cpu/op/convert_layout.hpp"
#include "ngraph/runtime/cpu/op/halide_op.hpp"
#include "ngraph/type/element_type.hpp"
......
src/ngraph/runtime/cpu/cpu_emitter.cpp
View file @ 5b9000ac
......@@ -118,7 +118,6 @@
#include "ngraph/runtime/cpu/op/batch_mat_mul_transpose.hpp"
#include "ngraph/runtime/cpu/op/batch_norm_relu.hpp"
#include "ngraph/runtime/cpu/op/bounded_relu.hpp"
#include "ngraph/op/experimental/compiled_kernel.hpp"
#include "ngraph/runtime/cpu/op/conv_add.hpp"
#include "ngraph/runtime/cpu/op/conv_relu.hpp"
#include "ngraph/runtime/cpu/op/convert_layout.hpp"
......
src/ngraph/runtime/cpu/cpu_external_function.cpp
View file @ 5b9000ac
......@@ -170,7 +170,6 @@
#include "ngraph/runtime/cpu/op/deconv.hpp"
#include "ngraph/runtime/cpu/op/group_conv_bias.hpp"
#include "ngraph/runtime/cpu/op/leaky_relu.hpp"
#include "ngraph/runtime/cpu/op/loop_kernel.hpp"
#include "ngraph/runtime/cpu/op/lstm.hpp"
#include "ngraph/runtime/cpu/op/matmul_bias.hpp"
#include "ngraph/runtime/cpu/op/max_pool_with_indices.hpp"
......@@ -427,8 +426,7 @@ static const runtime::cpu::OpMap dispatcher{
{TI(ngraph::op::And), &runtime::cpu::CPU_Emitter::emit<op::And>},
{TI(ngraph::op::Or), &runtime::cpu::CPU_Emitter::emit<op::Or>},
{TI(ngraph::op::CPULeakyRelu), &runtime::cpu::CPU_Emitter::emit<op::CPULeakyRelu>},
{TI(ngraph::runtime::cpu::op::LoopKernel),
&runtime::cpu::CPU_Emitter::emit<runtime::cpu::op::LoopKernel>},
{TI(ngraph::op::CompiledKernel), &runtime::cpu::CPU_Emitter::emit<op::CompiledKernel>},
{TI(ngraph::op::LRN), &runtime::cpu::CPU_Emitter::emit<ngraph::op::LRN>},
{TI(ngraph::op::GenerateMask), &runtime::cpu::CPU_Emitter::emit<ngraph::op::GenerateMask>},
{TI(ngraph::op::ConvolutionAdd), &runtime::cpu::CPU_Emitter::emit<op::ConvolutionAdd>},
......
src/ngraph/serializer.cpp
View file @ 5b9000ac
......@@ -64,9 +64,31 @@
#include "ngraph/op/experimental/quantized_dot_bias.hpp"
#include "ngraph/op/experimental/quantized_max_pool.hpp"
#include "ngraph/op/experimental/shape_of.hpp"
#include "ngraph/op/experimental/tile.hpp"
#include "ngraph/op/experimental/transpose.hpp"
#include "ngraph/op/floor.hpp"
#include "ngraph/op/fused/clamp.hpp"
#include "ngraph/op/fused/conv_fused.hpp"
#include "ngraph/op/fused/depth_to_space.hpp"
#include "ngraph/op/fused/elu.hpp"
#include "ngraph/op/fused/fake_quantize.hpp"
#include "ngraph/op/fused/gemm.hpp"
#include "ngraph/op/fused/grn.hpp"
#include "ngraph/op/fused/group_conv.hpp"
#include "ngraph/op/fused/hard_sigmoid.hpp"
#include "ngraph/op/fused/leaky_relu.hpp"
#include "ngraph/op/fused/mvn.hpp"
#include "ngraph/op/fused/normalize.hpp"
#include "ngraph/op/fused/prelu.hpp"
#include "ngraph/op/fused/scale_shift.hpp"
#include "ngraph/op/fused/shuffle_channels.hpp"
#include "ngraph/op/fused/space_to_depth.hpp"
#include "ngraph/op/fused/split.hpp"
#include "ngraph/op/fused/squared_difference.hpp"
#include "ngraph/op/fused/squeeze.hpp"
#include "ngraph/op/fused/unsqueeze.hpp"
#include "ngraph/op/gather.hpp"
#include "ngraph/op/gather_nd.hpp"
#include "ngraph/op/get_output_element.hpp"
#include "ngraph/op/greater.hpp"
#include "ngraph/op/greater_eq.hpp"
......@@ -97,6 +119,8 @@
#include "ngraph/op/result.hpp"
#include "ngraph/op/reverse.hpp"
#include "ngraph/op/reverse_sequence.hpp"
#include "ngraph/op/scatter_add.hpp"
#include "ngraph/op/scatter_nd_add.hpp"
#include "ngraph/op/select.hpp"
#include "ngraph/op/sigmoid.hpp"
#include "ngraph/op/sign.hpp"
......@@ -120,6 +144,14 @@ using namespace std;
using json = nlohmann::json;
using const_data_callback_t = shared_ptr<Node>(const string&, const element::Type&, const Shape&);
static bool s_serialize_output_shapes_enabled =
(std::getenv("NGRAPH_SERIALIZER_OUTPUT_SHAPES") != nullptr);
void ngraph::set_serialize_output_shapes(bool enable)
{
s_serialize_output_shapes_enabled = enable;
}
// This expands the op list in op_tbl.hpp into a list of enumerations that look like this:
// Abs,
// Acos,
......@@ -207,7 +239,7 @@ static json write_partial_shape(const PartialShape& s)
{
vals[i] = write_dimension(s[i]);
}
return vals;
return move(vals);
}
}
......@@ -228,6 +260,27 @@ static PartialShape read_partial_shape(const json& j)
}
}
static json write_auto_broadcast(const op::AutoBroadcastSpec& autob)
{
json j;
j["type"] = autob.m_type;
j["axis"] = autob.m_axis;
return j;
}
static op::AutoBroadcastSpec read_auto_broadcast(const json& j)
{
if (!j.is_object())
{
return op::AutoBroadcastSpec();
}
else
{
return op::AutoBroadcastSpec(static_cast<op::AutoBroadcastType>(j.at("type")),
j.at("axis").get<size_t>());
}
}
static json write_element_type(const ngraph::element::Type& n)
{
json j;
......@@ -276,6 +329,12 @@ void ngraph::serialize(const string& path, shared_ptr<ngraph::Function> func, si
}
void ngraph::serialize(ostream& out, shared_ptr<ngraph::Function> func, size_t indent)
{
out << ::serialize(func, indent, false);
}
#if defined ENABLE_CPIO_FILE
static void serialize_to_cpio(ostream& out, shared_ptr<ngraph::Function> func, size_t indent)
{
string j = ::serialize(func, indent, true);
cpio::Writer writer(out);
......@@ -295,6 +354,7 @@ void ngraph::serialize(ostream& out, shared_ptr<ngraph::Function> func, size_t i
true);
});
}
#endif
static string serialize(shared_ptr<ngraph::Function> func, size_t indent, bool binary_constant_data)
{
......@@ -428,6 +488,18 @@ static json write(const Function& f, bool binary_constant_data)
return function;
}
template <typename T>
T get_value(nlohmann::json js, const string& key)
{
T rc;
auto it = js.find(key);
if (it != js.end())
{
rc = it->get<T>();
}
return rc;
}
static shared_ptr<ngraph::Function>
read_function(const json& func_js,
unordered_map<string, shared_ptr<Function>>& function_map,
......@@ -444,28 +516,23 @@ static shared_ptr<ngraph::Function>
try
{
string node_name = node_js.at("name").get<string>();
string friendly_name;
auto it = node_js.find("friendly_name");
if (it != node_js.end())
{
friendly_name = it->get<string>();
}
string node_op = node_js.at("op").get<string>();
vector<string> node_inputs = node_js.at("inputs").get<vector<string>>();
vector<string> control_deps_inputs =
get_or_default<vector<string>>(node_js, "control_deps", vector<string>{});
vector<string> node_outputs = node_js.at("outputs").get<vector<string>>();
string friendly_name = get_value<string>(node_js, "friendly_name");
vector<string> node_inputs = get_value<vector<string>>(node_js, "inputs");
vector<string> control_deps_inputs = get_value<vector<string>>(node_js, "control_deps");
vector<string> node_outputs = get_value<vector<string>>(node_js, "outputs");
shared_ptr<Node> node;
vector<shared_ptr<Node>> args;
vector<shared_ptr<Node>> control_deps;
for (const string& name : node_inputs)
{
args.push_back(node_map.at(name));
}
#if !(defined(__GNUC__) && __GNUC__ == 4 && __GNUC_MINOR__ == 8)
#pragma GCC diagnostic push
#pragma GCC diagnostic error "-Wswitch"
#pragma GCC diagnostic error "-Wswitch-enum"
// #pragma GCC diagnostic error "-Wimplicit-fallthrough"
// #pragma GCC diagnostic error "-Wimplicit-fallthrough"
#endif
switch (get_typeid(node_op))
{
case OP_TYPEID::Abs:
......@@ -480,7 +547,8 @@ static shared_ptr<ngraph::Function>
}
case OP_TYPEID::Add:
{
node = make_shared<op::Add>(args[0], args[1]);
node =
make_shared<op::Add>(args[0], args[1], read_auto_broadcast(node_js["autob"]));
break;
}
case OP_TYPEID::All:
......@@ -496,7 +564,8 @@ static shared_ptr<ngraph::Function>
}
case OP_TYPEID::And:
{
node = make_shared<op::And>(args[0], args[1]);
node =
make_shared<op::And>(args[0], args[1], read_auto_broadcast(node_js["autob"]));
break;
}
case OP_TYPEID::Any:
......@@ -538,12 +607,16 @@ static shared_ptr<ngraph::Function>
auto padding_above = node_js.at("padding_above").get<vector<size_t>>();
auto include_padding_in_avg_computation =
node_js.at("include_padding_in_avg_computation").get<bool>();
op::PadType pad_type = node_js["pad_type"].empty()
? op::PadType::EXPLICIT
: static_cast<op::PadType>(node_js.at("pad_type"));
node = make_shared<op::AvgPool>(args[0],
window_shape,
window_movement_strides,
padding_below,
padding_above,
include_padding_in_avg_computation);
include_padding_in_avg_computation,
pad_type);
break;
}
case OP_TYPEID::AvgPoolBackprop:
......@@ -565,6 +638,12 @@ static shared_ptr<ngraph::Function>
include_padding_in_avg_computation);
break;
}
case OP_TYPEID::BatchMatMul:
{
node = make_shared<op::BatchMatMul>(args[0], args[1]);
break;
}
case OP_TYPEID::BatchNormTraining:
{
auto epsilon = node_js.at("eps").get<double>();
......@@ -611,6 +690,13 @@ static shared_ptr<ngraph::Function>
node = make_shared<op::Ceiling>(args[0]);
break;
}
case OP_TYPEID::Clamp:
{
const auto clamp_min = node_js.at("min").get<float>();
const auto clamp_max = node_js.at("max").get<float>();
node = make_shared<op::Clamp>(args[0], clamp_min, clamp_max);
break;
}
case OP_TYPEID::Concat:
{
auto axis = node_js.at("axis").get<size_t>();
......@@ -623,16 +709,8 @@ static shared_ptr<ngraph::Function>
node_js.count("element_type") == 0 ? node_js.at("value_type") : node_js;
auto element_type = read_element_type(type_node_js.at("element_type"));
auto shape = type_node_js.at("shape");
auto value_it = node_js.find("value");
if (value_it != node_js.end())
{
auto value = value_it->get<vector<string>>();
node = make_shared<op::Constant>(element_type, shape, value);
}
else
{
node = const_data_callback(node_name, element_type, shape);
}
auto value = node_js.at("value").get<vector<string>>();
node = make_shared<op::Constant>(element_type, shape, value);
break;
}
case OP_TYPEID::Convert:
......@@ -658,6 +736,10 @@ static shared_ptr<ngraph::Function>
data_dilation_strides_maybe = node_js["image_dilation_strides"];
}
op::PadType pad_type = node_js["pad_type"].empty()
? op::PadType::EXPLICIT
: static_cast<op::PadType>(node_js.at("pad_type"));
if (data_dilation_strides_maybe.empty())
{
node = make_shared<op::Convolution>(args[0],
......@@ -676,7 +758,8 @@ static shared_ptr<ngraph::Function>
window_dilation_strides,
padding_below,
padding_above,
data_dilation_strides_maybe.get<std::vector<size_t>>());
data_dilation_strides_maybe.get<std::vector<size_t>>(),
pad_type);
}
break;
}
......@@ -726,6 +809,75 @@ static shared_ptr<ngraph::Function>
data_dilation_strides_forward);
break;
}
case OP_TYPEID::ConvolutionBias:
{
auto window_movement_strides =
node_js.at("window_movement_strides").get<vector<size_t>>();
auto window_dilation_strides =
node_js.at("window_dilation_strides").get<vector<size_t>>();
auto padding_below = node_js.at("padding_below").get<vector<std::ptrdiff_t>>();
auto padding_above = node_js.at("padding_above").get<vector<std::ptrdiff_t>>();
auto data_dilation_strides =
node_js.at("data_dilation_strides").get<vector<size_t>>();
node = make_shared<op::ConvolutionBias>(args[0],
args[1],
args[2],
window_movement_strides,
window_dilation_strides,
padding_below,
padding_above,
data_dilation_strides);
break;
}
case OP_TYPEID::ConvolutionBiasAdd:
{
auto window_movement_strides =
node_js.at("window_movement_strides").get<vector<size_t>>();
auto window_dilation_strides =
node_js.at("window_dilation_strides").get<vector<size_t>>();
auto padding_below = node_js.at("padding_below").get<vector<std::ptrdiff_t>>();
auto padding_above = node_js.at("padding_above").get<vector<std::ptrdiff_t>>();
auto data_dilation_strides =
node_js.at("data_dilation_strides").get<vector<size_t>>();
node = make_shared<op::ConvolutionBiasAdd>(args[0],
args[1],
args[2],
args[3],
window_movement_strides,
window_dilation_strides,
padding_below,
padding_above,
data_dilation_strides);
break;
}
case OP_TYPEID::ConvolutionBiasBackpropFiltersBias:
{
auto filters_shape = node_js.at("filters_shape").get<vector<size_t>>();
auto bias_shape = node_js.at("bias_shape").get<vector<size_t>>();
auto window_movement_strides_forward =
node_js.at("window_movement_strides_forward").get<vector<size_t>>();
auto window_dilation_strides_forward =
node_js.at("window_dilation_strides_forward").get<vector<size_t>>();
auto padding_below_forward =
node_js.at("padding_below_forward").get<vector<std::ptrdiff_t>>();
auto padding_above_forward =
node_js.at("padding_above_forward").get<vector<std::ptrdiff_t>>();
auto data_dilation_strides_forward =
node_js.at("data_dilation_strides_forward").get<vector<size_t>>();
node = make_shared<op::ConvolutionBiasBackpropFiltersBias>(
args[0],
filters_shape,
bias_shape,
args[1],
window_movement_strides_forward,
window_dilation_strides_forward,
padding_below_forward,
padding_above_forward,
data_dilation_strides_forward);
break;
}
case OP_TYPEID::Cos:
{
node = make_shared<op::Cos>(args[0]);
......@@ -736,6 +888,12 @@ static shared_ptr<ngraph::Function>
node = make_shared<op::Cosh>(args[0]);
break;
}
case OP_TYPEID::DepthToSpace:
{
auto block_size = node_js.at("block_size").get<size_t>();
node = make_shared<op::DepthToSpace>(args[0], block_size);
break;
}
case OP_TYPEID::Dequantize:
{
auto type = read_element_type(node_js.at("type"));
......@@ -745,7 +903,8 @@ static shared_ptr<ngraph::Function>
}
case OP_TYPEID::Divide:
{
node = make_shared<op::Divide>(args[0], args[1]);
node = make_shared<op::Divide>(
args[0], args[1], read_auto_broadcast(node_js["autob"]));
break;
}
case OP_TYPEID::Dot:
......@@ -783,6 +942,11 @@ static shared_ptr<ngraph::Function>
node = make_shared<op::DynSlice>(args[0], args[1], args[2], args[3]);
break;
}
case OP_TYPEID::Elu:
{
node = make_shared<op::Elu>(args[0], args[1]);
break;
}
case OP_TYPEID::EmbeddingLookup:
{
node = make_shared<op::EmbeddingLookup>(args[0], args[1]);
......@@ -790,7 +954,8 @@ static shared_ptr<ngraph::Function>
}
case OP_TYPEID::Equal:
{
node = make_shared<op::Equal>(args[0], args[1]);
node =
make_shared<op::Equal>(args[0], args[1], read_auto_broadcast(node_js["autob"]));
break;
}
case OP_TYPEID::Erf:
......@@ -803,11 +968,39 @@ static shared_ptr<ngraph::Function>
node = make_shared<op::Exp>(args[0]);
break;
}
case OP_TYPEID::FakeQuantize:
{
size_t levels = node_js.at("levels").get<size_t>();
node = make_shared<op::FakeQuantize>(
args[0], args[1], args[2], args[3], args[4], levels);
break;
}
case OP_TYPEID::Floor:
{
node = make_shared<op::Floor>(args[0]);
break;
}
case OP_TYPEID::Gather:
{
auto axis = node_js.at("axis").get<size_t>();
node = make_shared<op::Gather>(args[0], args[1], axis);
break;
}
case OP_TYPEID::GatherND:
{
node = make_shared<op::GatherND>(args[0], args[1]);
break;
}
case OP_TYPEID::Gemm:
{
auto alpha = node_js.at("alpha").get<double>();
auto beta = node_js.at("beta").get<double>();
auto transA = node_js.at("transA").get<bool>();
auto transB = node_js.at("transB").get<bool>();
node =
make_shared<op::Gemm>(args[0], args[1], args[2], alpha, beta, transA, transB);
break;
}
case OP_TYPEID::GenerateMask:
{
auto output_shape = node_js.at("output_shape").get<vector<size_t>>();
......@@ -826,22 +1019,71 @@ static shared_ptr<ngraph::Function>
}
case OP_TYPEID::Greater:
{
node = make_shared<op::Greater>(args[0], args[1]);
node = make_shared<op::Greater>(
args[0], args[1], read_auto_broadcast(node_js["autob"]));
break;
}
case OP_TYPEID::GreaterEq:
{
node = make_shared<op::GreaterEq>(args[0], args[1]);
node = make_shared<op::GreaterEq>(
args[0], args[1], read_auto_broadcast(node_js["autob"]));
break;
}
case OP_TYPEID::GRN:
{
auto bias = node_js.at("bias").get<float>();
node = make_shared<op::GRN>(args[0], bias);
break;
}
case OP_TYPEID::HardSigmoid:
{
auto alpha = node_js.at("alpha").get<float>();
auto beta = node_js.at("beta").get<float>();
node = make_shared<op::HardSigmoid>(args[0], alpha, beta);
break;
}
case OP_TYPEID::GroupConvolution:
{
auto window_movement_strides =
node_js.at("window_movement_strides").get<vector<size_t>>();
auto window_dilation_strides =
node_js.at("window_dilation_strides").get<vector<size_t>>();
auto padding_below = node_js.at("padding_below").get<vector<std::ptrdiff_t>>();
auto padding_above = node_js.at("padding_above").get<vector<std::ptrdiff_t>>();
auto data_dilation_strides =
node_js.at("data_dilation_strides").get<vector<size_t>>();
auto groups = node_js.at("groups").get<size_t>();
op::PadType pad_type = node_js["pad_type"].empty()
? op::PadType::EXPLICIT
: static_cast<op::PadType>(node_js.at("pad_type"));
node = make_shared<op::GroupConvolution>(args[0],
args[1],
window_movement_strides,
window_dilation_strides,
padding_below,
padding_above,
data_dilation_strides,
groups,
pad_type);
break;
}
case OP_TYPEID::LeakyRelu:
{
node = make_shared<op::LeakyRelu>(args[0], args[1]);
break;
}
case OP_TYPEID::Less:
{
node = make_shared<op::Less>(args[0], args[1]);
node =
make_shared<op::Less>(args[0], args[1], read_auto_broadcast(node_js["autob"]));
break;
}
case OP_TYPEID::LessEq:
{
node = make_shared<op::LessEq>(args[0], args[1]);
node = make_shared<op::LessEq>(
args[0], args[1], read_auto_broadcast(node_js["autob"]));
break;
}
case OP_TYPEID::Log:
......@@ -873,6 +1115,9 @@ static shared_ptr<ngraph::Function>
// omitted.
auto padding_below_maybe = node_js["padding_below"];
auto padding_above_maybe = node_js["padding_above"];
op::PadType pad_type = node_js["pad_type"].empty()
? op::PadType::EXPLICIT
: static_cast<op::PadType>(node_js.at("pad_type"));
if (padding_below_maybe.empty() && !padding_above_maybe.empty())
{
throw runtime_error(
......@@ -891,7 +1136,8 @@ static shared_ptr<ngraph::Function>
window_shape,
window_movement_strides,
padding_below,
padding_above);
padding_above,
pad_type);
}
else
{
......@@ -929,7 +1175,8 @@ static shared_ptr<ngraph::Function>
}
case OP_TYPEID::Maximum:
{
node = make_shared<op::Maximum>(args[0], args[1]);
node = make_shared<op::Maximum>(
args[0], args[1], read_auto_broadcast(node_js["autob"]));
break;
}
case OP_TYPEID::Min:
......@@ -940,12 +1187,22 @@ static shared_ptr<ngraph::Function>
}
case OP_TYPEID::Minimum:
{
node = make_shared<op::Minimum>(args[0], args[1]);
node = make_shared<op::Minimum>(
args[0], args[1], read_auto_broadcast(node_js["autob"]));
break;
}
case OP_TYPEID::Multiply:
{
node = make_shared<op::Multiply>(args[0], args[1]);
node = make_shared<op::Multiply>(
args[0], args[1], read_auto_broadcast(node_js["autob"]));
break;
}
case OP_TYPEID::MVN:
{
auto normalize_variance = node_js.at("normalize_variance").get<bool>();
auto across_channels = node_js.at("across_channels").get<bool>();
auto eps = node_js.at("eps").get<double>();
node = make_shared<op::MVN>(args[0], normalize_variance, across_channels, eps);
break;
}
case OP_TYPEID::Negative:
......@@ -953,9 +1210,19 @@ static shared_ptr<ngraph::Function>
node = make_shared<op::Negative>(args[0]);
break;
}
case OP_TYPEID::Normalize:
{
bool across_spatial = node_js.at("across_spatial").get<bool>();
bool channel_shared = node_js.at("channel_shared").get<bool>();
float eps = node_js.at("eps").get<float>();
node = make_shared<op::Normalize>(
args[0], args[1], across_spatial, channel_shared, eps);
break;
}
case OP_TYPEID::NotEqual:
{
node = make_shared<op::NotEqual>(args[0], args[1]);
node = make_shared<op::NotEqual>(
args[0], args[1], read_auto_broadcast(node_js["autob"]));
break;
}
case OP_TYPEID::Not:
......@@ -972,7 +1239,7 @@ static shared_ptr<ngraph::Function>
}
case OP_TYPEID::Or:
{
node = make_shared<op::Or>(args[0], args[1]);
node = make_shared<op::Or>(args[0], args[1], read_auto_broadcast(node_js["autob"]));
break;
}
case OP_TYPEID::Pad:
......@@ -983,15 +1250,11 @@ static shared_ptr<ngraph::Function>
// This is a legacy field whose functionality is no longer supported. The new
// behavior is equivalent to interior padding of 0, so we will accept it under
// those conditions.
auto padding_interior_maybe = node_js.find("padding_interior");
if (padding_interior_maybe != node_js.end())
{
auto padding_interior = padding_interior_maybe->get<vector<size_t>>();
NGRAPH_ASSERT(std::all_of(padding_interior.begin(),
padding_interior.end(),
[](size_t s) { return s == 0; }))
<< "Legacy padding_interior field must be zero everywhere.";
}
auto padding_interior = get_value<vector<size_t>>(node_js, "padding_interior");
NGRAPH_CHECK(std::all_of(padding_interior.begin(),
padding_interior.end(),
[](size_t s) { return s == 0; }),
"Legacy padding_interior field must be zero everywhere.");
auto pad_mode = node_js.count("pad_mode") == 0
? op::PadMode::CONSTANT
......@@ -1030,7 +1293,8 @@ static shared_ptr<ngraph::Function>
}
case OP_TYPEID::Power:
{
node = make_shared<op::Power>(args[0], args[1]);
node =
make_shared<op::Power>(args[0], args[1], read_auto_broadcast(node_js["autob"]));
break;
}
case OP_TYPEID::PRelu:
......@@ -1167,6 +1431,21 @@ static shared_ptr<ngraph::Function>
node = make_shared<op::ScalarConstantLike>(args[0], value);
break;
}
case OP_TYPEID::ScaleShift:
{
node = make_shared<op::ScaleShift>(args[0], args[1], args[2]);
break;
}
case OP_TYPEID::ScatterAdd:
{
node = make_shared<op::ScatterAdd>(args[0], args[1], args[2]);
break;
}
case OP_TYPEID::ScatterNDAdd:
{
node = make_shared<op::ScatterNDAdd>(args[0], args[1], args[2]);
break;
}
case OP_TYPEID::Select:
{
node = make_shared<op::Select>(args[0], args[1], args[2]);
......@@ -1177,6 +1456,13 @@ static shared_ptr<ngraph::Function>
node = make_shared<op::ShapeOf>(args[0]);
break;
}
case OP_TYPEID::ShuffleChannels:
{
const auto axis = node_js.at("axis").get<size_t>();
const auto groups = node_js.at("groups").get<size_t>();
node = make_shared<op::ShuffleChannels>(args[0], axis, groups);
break;
}
case OP_TYPEID::Sigmoid:
{
node = make_shared<op::Sigmoid>(args[0]);
......@@ -1216,14 +1502,38 @@ static shared_ptr<ngraph::Function>
node = make_shared<op::Softmax>(args[0], softmax_axes);
break;
}
case OP_TYPEID::SpaceToDepth:
{
auto block_size = node_js.at("block_size").get<size_t>();
node = make_shared<op::SpaceToDepth>(args[0], block_size);
break;
}
case OP_TYPEID::Split:
{
const auto axis = node_js.at("axis").get<size_t>();
const auto splits = node_js.at("splits").get<vector<size_t>>();
node = make_shared<op::Split>(args[0], axis, splits);
break;
}
case OP_TYPEID::Sqrt:
{
node = make_shared<op::Sqrt>(args[0]);
break;
}
case OP_TYPEID::SquaredDifference:
{
node = make_shared<op::SquaredDifference>(args[0], args[1]);
break;
}
case OP_TYPEID::Squeeze:
{
node = make_shared<op::Squeeze>(args[0], args[1]);
break;
}
case OP_TYPEID::Subtract:
{
node = make_shared<op::Subtract>(args[0], args[1]);
node = make_shared<op::Subtract>(
args[0], args[1], read_auto_broadcast(node_js["autob"]));
break;
}
case OP_TYPEID::Sum:
......@@ -1242,6 +1552,11 @@ static shared_ptr<ngraph::Function>
node = make_shared<op::Tanh>(args[0]);
break;
}
case OP_TYPEID::Tile:
{
node = make_shared<op::Tile>(args[0], args[1]);
break;
}
case OP_TYPEID::TopK:
{
auto top_k_axis = node_js.at("top_k_axis").get<size_t>();
......@@ -1261,6 +1576,11 @@ static shared_ptr<ngraph::Function>
node = make_shared<op::StopGradient>(args[0]);
break;
}
case OP_TYPEID::Unsqueeze:
{
node = make_shared<op::Unsqueeze>(args[0], args[1]);
break;
}
case OP_TYPEID::UnknownOp:
{
stringstream ss;
......@@ -1268,7 +1588,9 @@ static shared_ptr<ngraph::Function>
throw runtime_error(ss.str());
}
}
#if !(defined(__GNUC__) && (__GNUC__ == 4 && __GNUC_MINOR__ == 8))
#pragma GCC diagnostic pop
#endif
for (const string& name : control_deps_inputs)
{
......@@ -1348,24 +1670,33 @@ static json write(const Node& n, bool binary_constant_data)
json control_deps = json::array();
json outputs = json::array();
for (const descriptor::Input& input : n.get_inputs())
for (auto& input : n.inputs())
{
inputs.push_back(input.get_output().get_node()->get_name());
inputs.push_back(input.get_source_output().get_node()->get_name());
}
for (auto cdep : n.get_control_dependencies())
{
control_deps.push_back(cdep->get_name());
}
for (size_t i = 0; i < n.get_output_size(); ++i)
for (auto& output : n.outputs())
{
outputs.push_back(n.get_output_tensor(i).get_name());
outputs.push_back(output.get_tensor().get_name());
}
node["inputs"] = inputs;
node["control_deps"] = control_deps;
node["outputs"] = outputs;
if (!inputs.empty())
{
node["inputs"] = inputs;
}
if (!control_deps.empty())
{
node["control_deps"] = control_deps;
}
if (!outputs.empty())
{
node["outputs"] = outputs;
}
if (std::getenv("NGRAPH_SERIALIZER_OUTPUT_SHAPES") != nullptr)
if (s_serialize_output_shapes_enabled)
{
json output_shapes = json::array();
for (size_t i = 0; i < n.get_output_size(); ++i)
......@@ -1376,17 +1707,26 @@ static json write(const Node& n, bool binary_constant_data)
}
string node_op = n.description();
#if !(defined(__GNUC__) && (__GNUC__ == 4 && __GNUC_MINOR__ == 8))
#pragma GCC diagnostic push
#pragma GCC diagnostic error "-Wswitch"
#pragma GCC diagnostic error "-Wswitch-enum"
// #pragma GCC diagnostic error "-Wimplicit-fallthrough"
// #pragma GCC diagnostic error "-Wimplicit-fallthrough"
#endif
switch (get_typeid(node_op))
{
case OP_TYPEID::Abs: { break;
}
case OP_TYPEID::Acos: { break;
}
case OP_TYPEID::Add: { break;
case OP_TYPEID::Add:
{
auto tmp = dynamic_cast<const op::Add*>(&n);
if (tmp->get_autob().m_type != op::AutoBroadcastType::NONE)
{
node["autob"] = write_auto_broadcast(tmp->get_autob());
}
break;
}
case OP_TYPEID::ArgMin:
{
......@@ -1410,7 +1750,14 @@ static json write(const Node& n, bool binary_constant_data)
}
case OP_TYPEID::AllReduce: { break;
}
case OP_TYPEID::And: { break;
case OP_TYPEID::And:
{
auto tmp = dynamic_cast<const op::And*>(&n);
if (tmp->get_autob().m_type != op::AutoBroadcastType::NONE)
{
node["autob"] = write_auto_broadcast(tmp->get_autob());
}
break;
}
case OP_TYPEID::Any:
{
......@@ -1430,6 +1777,7 @@ static json write(const Node& n, bool binary_constant_data)
node["padding_below"] = tmp->get_padding_below();
node["padding_above"] = tmp->get_padding_above();
node["include_padding_in_avg_computation"] = tmp->get_include_padding_in_avg_computation();
node["pad_type"] = tmp->get_pad_type();
break;
}
case OP_TYPEID::AvgPoolBackprop:
......@@ -1443,6 +1791,8 @@ static json write(const Node& n, bool binary_constant_data)
node["include_padding_in_avg_computation"] = tmp->get_include_padding_in_avg_computation();
break;
}
case OP_TYPEID::BatchMatMul: { break;
}
case OP_TYPEID::BatchNormTraining:
{
auto tmp = dynamic_cast<const op::BatchNormTraining*>(&n);
......@@ -1478,6 +1828,13 @@ static json write(const Node& n, bool binary_constant_data)
}
case OP_TYPEID::Ceiling: { break;
}
case OP_TYPEID::Clamp:
{
auto tmp = dynamic_cast<const op::Clamp*>(&n);
node["min"] = tmp->get_min();
node["max"] = tmp->get_max();
break;
}
case OP_TYPEID::Concat:
{
auto tmp = dynamic_cast<const op::Concat*>(&n);
......@@ -1487,7 +1844,13 @@ static json write(const Node& n, bool binary_constant_data)
case OP_TYPEID::Constant:
{
auto tmp = dynamic_cast<const op::Constant*>(&n);
if (!binary_constant_data)
if (tmp->are_all_data_elements_bitwise_identical())
{
vector<string> vs;
vs.push_back(tmp->convert_value_to_string(0));
node["value"] = vs;
}
else
{
node["value"] = tmp->get_value_strings();
}
......@@ -1509,6 +1872,7 @@ static json write(const Node& n, bool binary_constant_data)
node["padding_below"] = tmp->get_padding_below();
node["padding_above"] = tmp->get_padding_above();
node["data_dilation_strides"] = tmp->get_data_dilation_strides();
node["pad_type"] = tmp->get_pad_type();
break;
}
case OP_TYPEID::ConvolutionBackpropData:
......@@ -1533,6 +1897,38 @@ static json write(const Node& n, bool binary_constant_data)
node["data_dilation_strides_forward"] = tmp->get_data_dilation_strides_forward();
break;
}
case OP_TYPEID::ConvolutionBias:
{
auto tmp = dynamic_cast<const op::ConvolutionBias*>(&n);
node["window_movement_strides"] = tmp->get_window_movement_strides();
node["window_dilation_strides"] = tmp->get_window_dilation_strides();
node["padding_below"] = tmp->get_padding_below();
node["padding_above"] = tmp->get_padding_above();
node["data_dilation_strides"] = tmp->get_data_dilation_strides();
break;
}
case OP_TYPEID::ConvolutionBiasAdd:
{
auto tmp = dynamic_cast<const op::ConvolutionBiasAdd*>(&n);
node["window_movement_strides"] = tmp->get_window_movement_strides();
node["window_dilation_strides"] = tmp->get_window_dilation_strides();
node["padding_below"] = tmp->get_padding_below();
node["padding_above"] = tmp->get_padding_above();
node["data_dilation_strides"] = tmp->get_data_dilation_strides();
break;
}
case OP_TYPEID::ConvolutionBiasBackpropFiltersBias:
{
auto tmp = dynamic_cast<const op::ConvolutionBiasBackpropFiltersBias*>(&n);
node["filters_shape"] = tmp->get_filters_shape();
node["bias_shape"] = tmp->get_bias_shape();
node["window_movement_strides_forward"] = tmp->get_window_movement_strides_forward();
node["window_dilation_strides_forward"] = tmp->get_window_dilation_strides_forward();
node["padding_below_forward"] = tmp->get_padding_below_forward();
node["padding_above_forward"] = tmp->get_padding_above_forward();
node["data_dilation_strides_forward"] = tmp->get_data_dilation_strides_forward();
break;
}
case OP_TYPEID::Cos: { break;
}
case OP_TYPEID::Cosh: { break;
......@@ -1544,7 +1940,21 @@ static json write(const Node& n, bool binary_constant_data)
node["axes"] = tmp->get_axes();
break;
}
case OP_TYPEID::Divide: { break;
case OP_TYPEID::DepthToSpace:
{
auto tmp = dynamic_cast<const op::DepthToSpace*>(&n);
node["type"] = write_element_type(tmp->get_element_type());
node["block_size"] = tmp->get_block_size();
break;
}
case OP_TYPEID::Divide:
{
auto tmp = dynamic_cast<const op::Divide*>(&n);
if (tmp->get_autob().m_type != op::AutoBroadcastType::NONE)
{
node["autob"] = write_auto_broadcast(tmp->get_autob());
}
break;
}
case OP_TYPEID::Dot:
{
......@@ -1560,22 +1970,54 @@ static json write(const Node& n, bool binary_constant_data)
}
case OP_TYPEID::DynSlice: { break;
}
case OP_TYPEID::Elu: { break;
}
case OP_TYPEID::EmbeddingLookup: { break;
}
case OP_TYPEID::Equal: { break;
case OP_TYPEID::Equal:
{
auto tmp = dynamic_cast<const op::Equal*>(&n);
if (tmp->get_autob().m_type != op::AutoBroadcastType::NONE)
{
node["autob"] = write_auto_broadcast(tmp->get_autob());
}
break;
}
case OP_TYPEID::Erf: { break;
}
case OP_TYPEID::Exp: { break;
}
case OP_TYPEID::FakeQuantize:
{
auto tmp = dynamic_cast<const op::FakeQuantize*>(&n);
node["levels"] = tmp->get_levels();
break;
}
case OP_TYPEID::Floor: { break;
}
case OP_TYPEID::Gather:
{
auto tmp = dynamic_cast<const op::Gather*>(&n);
node["axis"] = tmp->get_axis();
break;
}
case OP_TYPEID::GatherND: { break;
}
case OP_TYPEID::GetOutputElement:
{
auto tmp = dynamic_cast<const op::GetOutputElement*>(&n);
node["n"] = tmp->get_n();
break;
}
case OP_TYPEID::Gemm:
{
auto tmp = dynamic_cast<const op::Gemm*>(&n);
node["alpha"] = tmp->get_alpha();
node["beta"] = tmp->get_beta();
node["transA"] = tmp->get_transA();
node["transB"] = tmp->get_transB();
break;
}
case OP_TYPEID::GenerateMask:
{
auto tmp = dynamic_cast<const op::GenerateMask*>(&n);
......@@ -1585,13 +2027,68 @@ static json write(const Node& n, bool binary_constant_data)
node["probability"] = tmp->get_probability();
break;
}
case OP_TYPEID::Greater: { break;
case OP_TYPEID::Greater:
{
auto tmp = dynamic_cast<const op::Greater*>(&n);
if (tmp->get_autob().m_type != op::AutoBroadcastType::NONE)
{
node["autob"] = write_auto_broadcast(tmp->get_autob());
}
break;
}
case OP_TYPEID::GreaterEq: { break;
case OP_TYPEID::GreaterEq:
{
auto tmp = dynamic_cast<const op::GreaterEq*>(&n);
if (tmp->get_autob().m_type != op::AutoBroadcastType::NONE)
{
node["autob"] = write_auto_broadcast(tmp->get_autob());
}
break;
}
case OP_TYPEID::Less: { break;
case OP_TYPEID::GRN:
{
auto tmp = dynamic_cast<const op::GRN*>(&n);
node["bias"] = tmp->get_bias();
break;
}
case OP_TYPEID::LessEq: { break;
case OP_TYPEID::HardSigmoid:
{
auto tmp = dynamic_cast<const op::HardSigmoid*>(&n);
node["alpha"] = tmp->get_alpha();
node["beta"] = tmp->get_beta();
break;
}
case OP_TYPEID::GroupConvolution:
{
auto tmp = dynamic_cast<const op::GroupConvolution*>(&n);
node["window_movement_strides"] = tmp->get_window_movement_strides();
node["window_dilation_strides"] = tmp->get_window_dilation_strides();
node["padding_below"] = tmp->get_padding_below();
node["padding_above"] = tmp->get_padding_above();
node["data_dilation_strides"] = tmp->get_data_dilation_strides();
node["groups"] = tmp->get_groups();
node["pad_type"] = tmp->get_pad_type();
break;
}
case OP_TYPEID::LeakyRelu: { break;
}
case OP_TYPEID::Less:
{
auto tmp = dynamic_cast<const op::Less*>(&n);
if (tmp->get_autob().m_type != op::AutoBroadcastType::NONE)
{
node["autob"] = write_auto_broadcast(tmp->get_autob());
}
break;
}
case OP_TYPEID::LessEq:
{
auto tmp = dynamic_cast<const op::LessEq*>(&n);
if (tmp->get_autob().m_type != op::AutoBroadcastType::NONE)
{
node["autob"] = write_auto_broadcast(tmp->get_autob());
}
break;
}
case OP_TYPEID::Log: { break;
}
......@@ -1617,6 +2114,7 @@ static json write(const Node& n, bool binary_constant_data)
node["window_movement_strides"] = tmp->get_window_movement_strides();
node["padding_below"] = tmp->get_padding_below();
node["padding_above"] = tmp->get_padding_above();
node["pad_type"] = tmp->get_pad_type();
break;
}
case OP_TYPEID::MaxPoolBackprop:
......@@ -1628,7 +2126,14 @@ static json write(const Node& n, bool binary_constant_data)
node["padding_above"] = tmp->get_padding_above();
break;
}
case OP_TYPEID::Maximum: { break;
case OP_TYPEID::Maximum:
{
auto tmp = dynamic_cast<const op::Maximum*>(&n);
if (tmp->get_autob().m_type != op::AutoBroadcastType::NONE)
{
node["autob"] = write_auto_broadcast(tmp->get_autob());
}
break;
}
case OP_TYPEID::Min:
{
......@@ -1636,13 +2141,50 @@ static json write(const Node& n, bool binary_constant_data)
node["reduction_axes"] = tmp->get_reduction_axes();
break;
}
case OP_TYPEID::Minimum: { break;
case OP_TYPEID::Minimum:
{
auto tmp = dynamic_cast<const op::Minimum*>(&n);
if (tmp->get_autob().m_type != op::AutoBroadcastType::NONE)
{
node["autob"] = write_auto_broadcast(tmp->get_autob());
}
break;
}
case OP_TYPEID::Multiply: { break;
case OP_TYPEID::Multiply:
{
auto tmp = dynamic_cast<const op::Multiply*>(&n);
if (tmp->get_autob().m_type != op::AutoBroadcastType::NONE)
{
node["autob"] = write_auto_broadcast(tmp->get_autob());
}
break;
}
case OP_TYPEID::MVN:
{
auto tmp = dynamic_cast<const op::MVN*>(&n);
node["normalize_variance"] = tmp->get_normalize_variance();
node["across_channels"] = tmp->get_across_channels();
node["eps"] = tmp->get_eps();
break;
}
case OP_TYPEID::Negative: { break;
}
case OP_TYPEID::NotEqual: { break;
case OP_TYPEID::Normalize:
{
auto tmp = dynamic_cast<const op::Normalize*>(&n);
node["across_spatial"] = tmp->get_across_spatial();
node["channel_shared"] = tmp->get_channel_shared();
node["eps"] = tmp->get_eps();
break;
}
case OP_TYPEID::NotEqual:
{
auto tmp = dynamic_cast<const op::NotEqual*>(&n);
if (tmp->get_autob().m_type != op::AutoBroadcastType::NONE)
{
node["autob"] = write_auto_broadcast(tmp->get_autob());
}
break;
}
case OP_TYPEID::Not: { break;
}
......@@ -1653,7 +2195,14 @@ static json write(const Node& n, bool binary_constant_data)
node["one_hot_axis"] = tmp->get_one_hot_axis();
break;
}
case OP_TYPEID::Or: { break;
case OP_TYPEID::Or:
{
auto tmp = dynamic_cast<const op::Or*>(&n);
if (tmp->get_autob().m_type != op::AutoBroadcastType::NONE)
{
node["autob"] = write_auto_broadcast(tmp->get_autob());
}
break;
}
case OP_TYPEID::Pad:
{
......@@ -1696,7 +2245,14 @@ static json write(const Node& n, bool binary_constant_data)
node["reduction_axes"] = tmp->get_reduction_axes();
break;
}
case OP_TYPEID::Power: { break;
case OP_TYPEID::Power:
{
auto tmp = dynamic_cast<const op::Power*>(&n);
if (tmp->get_autob().m_type != op::AutoBroadcastType::NONE)
{
node["autob"] = write_auto_broadcast(tmp->get_autob());
}
break;
}
case OP_TYPEID::Quantize:
{
......@@ -1789,10 +2345,23 @@ static json write(const Node& n, bool binary_constant_data)
node["element_type"] = write_element_type(constant->get_element_type());
break;
}
case OP_TYPEID::ScaleShift: { break;
}
case OP_TYPEID::ScatterAdd: { break;
}
case OP_TYPEID::ScatterNDAdd: { break;
}
case OP_TYPEID::Select: { break;
}
case OP_TYPEID::ShapeOf: { break;
}
case OP_TYPEID::ShuffleChannels:
{
const auto tmp = dynamic_cast<const op::ShuffleChannels*>(&n);
node["axis"] = tmp->get_axis();
node["groups"] = tmp->get_groups();
break;
}
case OP_TYPEID::Sigmoid: { break;
}
case OP_TYPEID::SigmoidBackprop: { break;
......@@ -1811,11 +2380,36 @@ static json write(const Node& n, bool binary_constant_data)
node["strides"] = tmp->get_strides();
break;
}
case OP_TYPEID::SpaceToDepth:
{
auto tmp = dynamic_cast<const op::SpaceToDepth*>(&n);
node["type"] = write_element_type(tmp->get_element_type());
node["block_size"] = tmp->get_block_size();
break;
}
case OP_TYPEID::Split:
{
auto tmp = dynamic_cast<const op::Split*>(&n);
node["axis"] = tmp->get_axis();
node["splits"] = tmp->get_splits();
break;
}
case OP_TYPEID::Sqrt: { break;
}
case OP_TYPEID::SquaredDifference: { break;
}
case OP_TYPEID::Squeeze: { break;
}
case OP_TYPEID::StopGradient: { break;
}
case OP_TYPEID::Subtract: { break;
case OP_TYPEID::Subtract:
{
auto tmp = dynamic_cast<const op::Subtract*>(&n);
if (tmp->get_autob().m_type != op::AutoBroadcastType::NONE)
{
node["autob"] = write_auto_broadcast(tmp->get_autob());
}
break;
}
case OP_TYPEID::Sum:
{
......@@ -1833,6 +2427,8 @@ static json write(const Node& n, bool binary_constant_data)
}
case OP_TYPEID::Tanh: { break;
}
case OP_TYPEID::Tile: { break;
}
case OP_TYPEID::TopK:
{
auto tmp = dynamic_cast<const op::TopK*>(&n);
......@@ -1844,10 +2440,14 @@ static json write(const Node& n, bool binary_constant_data)
}
case OP_TYPEID::Transpose: { break;
}
case OP_TYPEID::Unsqueeze: { break;
}
case OP_TYPEID::UnknownOp: { break;
}
}
#if !(defined(__GNUC__) && (__GNUC__ == 4 && __GNUC_MINOR__ == 8))
#pragma GCC diagnostic pop
#endif
return node;
}
src/ngraph/util.hpp
View file @ 5b9000ac
......@@ -44,6 +44,7 @@ namespace ngraph
namespace runtime
{
class Backend;
class Value;
}
std::string to_cplusplus_sourcecode_literal(bool val);
......
test/cpu_fusion.cpp
View file @ 5b9000ac
......@@ -74,7 +74,6 @@
#include "ngraph/runtime/cpu/op/sigmoid_mul.hpp"
#include "ngraph/runtime/cpu/op/update_slice.hpp"
#include "ngraph/runtime/cpu/pass/cpu_fusion.hpp"
#include "ngraph/runtime/cpu/pass/cpu_loop_kernel_fusion.hpp"
#include "ngraph/runtime/cpu/pass/cpu_mat_fusion.hpp"
#include "ngraph/runtime/cpu/pass/cpu_post_layout_optimizations.hpp"
#include "ngraph/runtime/cpu/pass/cpu_rnn_fusion.hpp"
......
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